Senior Engineer - AI Workflows

Xero4 days ago

Location

CAN: British Columbia Remote

Workplace

Remote

Type

Full Time

Salary

CAD 180,000 – 210,000

Level

Senior

Role

Senior Engineer

Posted

Jul 21, 2026

Full TimeRemoteSenior

The role

Summary

Senior Engineer specializing in AI workflows at Xero's Internal AI Accelerator Squad, focused on designing and operationalizing agentic AI systems that automate complex enterprise workflows. You will embed within business teams to co-design intelligent automation solutions using large language models and enterprise integrations, establishing patterns and guardrails for AI-native operations across the global organization. This role requires production-grade experience with AI systems, cloud infrastructure, and the ability to translate business pain points into scalable technical solutions.

What you'll do

Design and Iterate on Agentic AI Systems: Lead the architectural design and continuous iteration of AI agents and workflow orchestrations leveraging advanced language models like Claude and Gemini, employing sophisticated prompt engineering and model configuration strategies to optimize agent behavior and reliability in production environments.

Enterprise Platform Integration: Architect and implement robust integrations between AI systems and core enterprise applications including Salesforce, Workday, Slack, and NetSuite, ensuring seamless data flow, security compliance, and maintaining system reliability across distributed infrastructure.

Build Reusable Automation Patterns: Develop and document standardized automation patterns, shared templates, and best-practice frameworks that empower non-technical and technical stakeholders across the business to deploy AI-driven solutions independently, democratizing access to intelligent automation capabilities.

Establish Evaluation and Monitoring Frameworks: Design comprehensive evaluation metrics, monitoring dashboards, and performance tracking systems for AI workflows, implementing safety guardrails and blast radius controls to ensure reliability, auditability, and controlled degradation in production AI-native systems.

Technical Leadership and Coaching: Act as a technical mentor and evangelist within business teams, elevating AI fluency across the organization through collaborative pairing sessions, technical workshops, clear documentation, and hands-on guidance on AI-driven solution design patterns.

Forward-Deployed Problem Solving: Embed directly within various internal business teams to understand complex workflows, co-design solutions that remove manual toil, and drive end-to-end operationalization from discovery phase through production deployment, serving as a strategic technical partner.

What we look for

Technical

Production Application DevelopmentDemonstrated expertise building and operating production applications or automation systems, particularly with hands-on experience managing APIs, webhooks, cloud infrastructure, and designing systems for scale and reliability.

Large Language Model SystemsProven experience delivering AI-driven solutions in production, including building agents, copilots, retrieval-augmented generation (RAG) systems, or other LLM applications using platforms like Claude, Gemini, or equivalent enterprise AI models.

Programming LanguagesProficiency in Python or TypeScript with demonstrated ability to write clean, maintainable code, work with SDKs, manage asynchronous operations, and develop microservices or serverless architectures for AI workloads.

Enterprise Integration ArchitectureExperience integrating with enterprise platforms (CRM, ERP, HCM, communication tools), designing secure data pipelines, managing authentication flows, and ensuring compliance with enterprise security and auditing requirements.

Systems Design and SecurityStrong systems-thinking approach with capability to design AI systems emphasizing security, auditability, proper access controls, and controlled blast radius patterns to mitigate risks in autonomous agent deployments.

Education

Computer Science or Related FieldBachelor's degree in Computer Science, Software Engineering, or equivalent professional experience demonstrating core computer science fundamentals and software development expertise.

Continuous Learning in AI/MLCommitment to ongoing professional development in AI and machine learning, evidenced through courses, certifications, or self-directed learning in LLMs, prompt engineering, and AI systems design.

Experience

AI-Driven Solutions DeliveryTrack record of shipping AI-driven products or features from concept through production, including experience with prompt engineering, model selection, and implementing evaluation frameworks to measure AI system performance.

Stakeholder PartnershipDemonstrated success partnering with non-technical stakeholders, translating business requirements and pain points into elegant technical solution designs, and communicating complex technical concepts to diverse audiences.

Cloud Infrastructure OperationsHands-on experience deploying and maintaining applications on cloud platforms, managing infrastructure as code, implementing monitoring and logging solutions, and optimizing for performance and cost efficiency.

Technical MentorshipTrack record of uplifting team capabilities through technical leadership, mentoring junior engineers, and driving adoption of new technologies or architectural patterns across teams.

Skills

Required skills

PythonProduction-grade Python development for backend services, data processing, and AI system integration with proficiency in async programming and SDK usage.

TypeScriptFull-stack TypeScript development including type-safe API integrations, serverless functions, and real-time workflow orchestration in Node.js environments.

Large Language Models (Claude/Gemini)Hands-on experience with Claude or Gemini APIs, prompt engineering, model selection, and building production systems that leverage LLM capabilities for automation and intelligence.

API Design and IntegrationExpertise in designing RESTful and webhook-based integrations, managing authentication schemes (OAuth, API keys), error handling, and building robust data synchronization between systems.

Cloud InfrastructureProficiency with cloud platforms (AWS, GCP, Azure) for deploying AI workloads, managing databases, implementing monitoring, and designing scalable architectures.

Agentic AI WorkflowsExperience designing and implementing autonomous AI agents with state management, tool integration, decision logic, and monitoring capabilities for enterprise use cases.

Nice to have

Model Context Protocol (MCP)Familiarity with Model Context Protocol for standardizing AI system integrations and enabling flexible tool composition across different LLM platforms and applications.

Anthropic Claude EcosystemDeep knowledge of Claude models, function calling, vision capabilities, and extended context windows for building sophisticated AI applications.

Salesforce/Workday IntegrationPrior experience integrating with major enterprise platforms like Salesforce or Workday, understanding their data models and API constraints.

Slack Bot DevelopmentExperience building conversational interfaces and workflow automation through Slack apps, bolt frameworks, and interactive components.

RAG SystemsExperience implementing retrieval-augmented generation systems including vector databases, embedding models, and knowledge base construction for AI applications.

System ObservabilityProficiency implementing monitoring, logging, tracing, and alerting for complex distributed systems, including AI-specific metrics like inference latency and token utilization.

Workflow OrchestrationExperience with tools like Temporal, Airflow, or similar workflow orchestration platforms for managing complex multi-step automation sequences.

Agile Development PracticesExperience in rapid iteration cycles, rapid prototyping, and experimental development methodologies aligned with high-velocity team environments.

Compensation & benefits

Salary

CAD 180,000 – 210,000 (annual)

Stock options

Available

Benefits

Comprehensive Medical Coverage

Medical, dental, and vision insurance providing comprehensive healthcare coverage for you and your family, with competitive plan options and managed care networks.

Retirement Planning

401(k) matching program enabling tax-advantaged retirement savings with employer contribution matching to accelerate your long-term financial security.

Generous Time Off

21 days of paid time off annually plus wellbeing leave, volunteer leave, and paid parental leave supporting work-life balance and personal growth.

Equity Participation

Restricted Stock Unit (RSU) programs and performance-based equity incentives allowing you to share in Xero's success and build long-term wealth.

Flexible Work Arrangements

Hybrid work model with autonomy to work from home complemented by regular boost days in modern office spaces designed for collaboration and team connection.

Professional Development

Access to learning resources, technical conferences, and continuous education opportunities supporting career growth in emerging AI and cloud technologies.

Wellbeing Programs

Comprehensive wellbeing initiatives supporting physical, mental, and financial health including employee assistance programs and wellness resources.

Interview process

1
Initial Screening — Conversation with recruiter to discuss your background, AI experience, and alignment with the Internal AI Accelerator Squad's mission of rapid experimentation and agentic AI implementation.
2
Technical Discussion — Deep dive with senior engineers on your production experience with AI systems, large language models, and enterprise integrations. Expect discussion of architectural decisions, system design tradeoffs, and real-world challenges you've solved.
3
System Design Assessment — Design exercise focused on architecting an agentic AI workflow that integrates with enterprise platforms. You'll walk through your approach to security, auditability, monitoring, and failure handling in production systems.
4
Coding Exercise — Practical coding assessment in Python or TypeScript demonstrating your ability to build AI integrations, handle API interactions, and write production-quality code with proper error handling and testing.
5
Stakeholder Alignment Interview — Conversation with business stakeholders and product owners to assess your ability to translate business pain points into elegant technical solutions and your communication style with non-technical partners.
6
Senior Leadership Discussion — Final conversation with leadership to discuss your vision for democratizing AI across the organization, your mentorship philosophy, and how you'd contribute to Xero's transformation into an AI-native company.